Combining Multiple JSON Files into a Single Dictionary with Python

Learn how to efficiently merge multiple JSON files in Python, avoiding duplication and organizing your data effectively.
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Merging Multiple JSON Files into a Single Dictionary in Python

Working with JSON files can present its own set of challenges, especially when you are tasked with merging data from multiple files into one cohesive dataset. If you are dealing with identical layouts across these JSON files, the goal is to consolidate the information without duplicating any entries.

In this guide, we will look at how to accomplish this by merging three JSON files that each contain a results key with potentially overlapping data. This guide will provide clear steps and the Python code needed to achieve the desired result.

Understanding the Problem

Imagine you have a set of JSON files that contain engagement data structured under a results key. For example, the data in each file could look like this:

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The Challenge

You want to combine these JSON files into a single dictionary.

You need to ensure that there are no duplicate entries in the combined result.

The final output should only contain the data within the results key.

Step-by-Step Solution

Let’s break down the solution into manageable steps.

1. Load Required Libraries

You will be using Python's built-in json library for handling JSON data and os for file handling.

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2. Set Up the File Path

Begin by specifying the directory where your JSON files are stored.

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3. Initialize an Empty List

You will need a list to hold all the merged data.

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4. Loop Through Each File

Iterate over each JSON file, load its content, and extract the necessary data.

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5. Write the Output to a New JSON File

After looping through all files, save the combined result into a new output JSON file.

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Conclusion

This method effectively combines multiple JSON files into one, merging their data under the results key and ensuring that there are no duplicates. The final result is a single JSON file, containing all original entries from the input files.

As you advance to larger datasets in production, this method remains efficient, allowing for easy data manipulation and access within your analysis pipeline using pandas or similar libraries.

Happy coding!